Deep neural network acoustic models for spoken assessment applications

Author:

Cheng Jian,Chen Xin,Metallinou Angeliki

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modelling and Simulation,Software

Reference73 articles.

1. Abdel-Hamid, O., Mohamed, A., Jiang, H., Penn, G., 2012. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: Proc. of ICASSP, pp. 4277–4280.

2. Arizona Department of Education, 2014. Arizona English Language Learner Assessment (AZELLA). (accessed 18.08.14).

3. Balogh, J., Bernstein, J., Cheng, J., Townshend, B., 2007. Automatic evaluation of reading accuracy: assessing machine scores. In: Proc. of SLaTE, pp. 112–115.

4. Validation of automated scoring of oral reading;Balogh;Educat. Psychol. Measurement,2012

5. Bengio, Y., 2012. Practical recommendations for gradient-based training of deep architectures (Section 3.2). Tech. rep., arXiv.

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